Bone suppression for chest radiographs using deep learning

ABSTRACT

A system and method for generating a rib suppressed radiographic image using deep learning computation. The method includes using a convolutional neural network module trained with pairs of a chest x-ray image and its counterpart bone suppressed image. The bone suppressed image is obtained using a bone suppression algorithm applied to the chest x-ray image. The convolutional neural network module is then applied to a chest x-ray image or the bone suppressed image to generate an enhanced bone suppressed image.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Patent Application Ser. No.62/717,163, filed Aug. 10, 2018, in the name of Huo et al., and entitledBONE SUPPRESSION FOR CHEST RADIOGRAPHS USING DEEP LEARNING MODELS, whichis hereby incorporated by reference herein in its entirety.

This application is related in certain respects to U.S. Pat. No.8,913,817, issued Dec. 16, 2014, in the name of Huo et al., entitled RIBSUPPRESSION IN RADIOGRAPHIC IMAGES; U.S. Pat. No. 9,269,139, issued Feb.23, 2016, in the name of Huo et al., entitled RIB SUPPRESSION INRADIOGRAPHIC IMAGES; U.S. Pat. No. 9,659,390, issued May 23, 2017, inthe name of Huo et al., entitled TOMOSYNTHESIS RECONSTRUCTION WITH RIBSUPPRESSION; U.S. Pat. No. 9,668,712, issued Jun. 6, 2017, in the nameof Foos et al., entitled METHOD AND SYSTEM FOR QUANTITATIVE IMAGING; andU.S. Pat. No. 8,961,011, issued Feb. 24, 2015, in the name of Lalena,entitled MOBILE RADIOGRAPHY UNIT HAVING MULTIPLE MONITORS; all of whichare hereby incorporated by reference as if fully set forth herein intheir entirety.

BACKGROUND OF THE INVENTION

The disclosure relates generally to the field of medical imaging, and inparticular to bone suppression for chest radiographs. More specifically,the disclosure relates to a method of bone suppression for chestradiographs using deep learning convolutional networks, and programmodules for executing such deep learning convolutional algorithms.

BRIEF DESCRIPTION OF THE INVENTION

A system and method for generating a rib suppressed radiographic imageusing deep learning computation. The method includes using aconvolutional neural network module trained with pairs of a chest x-rayimage and its counterpart bone suppressed image, or, in other words,pairs of radiographic images including a starting image and a targetimage. The bone suppressed image is obtained using a known bonesuppression algorithm applied to the chest x-ray image. A knownconvolutional neural network module trained to suppress rib content isthen applied to a chest x-ray image or the bone suppressed image togenerate an enhanced bone suppressed chest x-ray image.

In one embodiment, a system includes an x-ray imaging system to capturea radiographic image. A processor is configured to apply a bonesuppression algorithm to the captured radiographic image to generate abone suppressed version of the captured radiographic image. A traineddeep learning module is trained by both the captured radiographic imageand the bone suppressed version of the captured radiographic image togenerate an enhanced bone suppressed image of the radiographic image.

In one embodiment, a system includes electronic memory for storing aradiographic image. A processor is configured to apply a bonesuppression algorithm to the stored radiographic image to generate abone suppressed version of the stored radiographic image. A deeplearning module receives the stored radiographic image and the bonesuppressed version of the stored radiographic image. The deep learningmodule is configured to be trained on the images to generate an enhancedbone suppressed image using the stored radiographic image.

In one embodiment, a system includes an x-ray imaging system to capturea current radiographic image. A deep learning module trained on aplurality of previously captured radiographic images and a correspondingplurality of bone-suppressed radiographic images generated from theplurality of previously captured radiographic images. A processor of thesystem is configured to apply the trained deep learning module to thecaptured current radiographic image to generate an enhancedbone-suppressed radiographic image.

In one embodiment, a method includes receiving a digital radiographicimage and applying a bone suppression algorithm to the received digitalradiographic image to generate a digital bone suppressed radiographicimage. A deep learning neural network module trained for suppressingbone regions of digital radiographic images is accessed to generate anenhanced digital bone suppressed radiographic image using the receiveddigital radiographic image.

In one embodiment, a method includes receiving a digital radiographicimage and applying a bone suppression algorithm to the received digitalradiographic image to generate a digital bone suppressed radiographicimage. A deep learning neural network module trained for suppressingbone regions of digital radiographic images is accessed to generate anenhanced digital bone suppressed radiographic image using the generateddigital bone suppressed radiographic image.

In one embodiment, a computer implemented method includes acquiring adigital radiographic image, applying a bone suppression algorithm to theacquired radiographic image to generate a bone suppressed radiographicimage. A convolutional neural network module trained using a pluralityof radiographic training images is applied to the bone suppressedradiographic image or the acquired digital radiographic image togenerate an enhanced bone suppressed radiographic image.

In at least one arrangement, there is provided an x-ray imaging systemto capture a medical image, a deep learning module, and a processorapplying a bone suppression algorithm to the captured medical image, andapplying the deep learning module to the bone suppressed capturedmedical image to generate an enhanced bone suppressed image.

In at least one arrangement, there is provided an x-ray imaging systemto capture a medical image, a deep learning module trained using aplurality of medical images and a plurality of bone-suppressed images. Aprocessor applies the trained deep learning module to the capturedmedical image to generate an enhanced medical image.

In at least one arrangement, there is provided an x-ray imaging systemto capture a medical image, a deep learning module trained using aplurality of medical images and a plurality of bone-suppressed imagesderived from at least some of the plurality of medical images, and aprocessor to apply the trained deep learning module to the capturedmedical image to generate an enhanced medical image.

In at least one arrangement, there is provided a method including thesteps of acquiring a digital medical image using an x-ray projectionimaging system, applying a bone suppression algorithm to the medicalimage to generate a bone suppressed image, providing a deep learningconvolutional neural network module trained using a plurality of medicalimages, applying the neural network module to the bone suppressed imageto generate an enhanced bone suppressed image, and displaying, storing,or transmitting the enhanced bone suppressed image.

In at least one arrangement, there is provided a method including thesteps of acquiring a digital medical image using an x-ray imagingsystem, applying a bone suppression algorithm to the medical image togenerate a bone suppressed image, providing a plurality of trainingimages, providing a convolutional neural network module trained using atleast some of the plurality of training images, applying theconvolutional neural network module to a bone suppressed image togenerate an enhanced bone suppressed image, and displaying, storing, ortransmitting the enhanced bone suppressed image.

In at least one method the steps include providing a plurality oftraining images including providing a plurality of digital medicalimages and providing a plurality of bone-suppressed images derived fromat least some of the plurality of digital medical images.

In at least one method, providing the plurality of training imagesincludes providing a plurality of chest x-ray images and a plurality ofrib suppressed chest x-ray images.

In at least one method, providing the plurality of training imagesincludes providing (i) a plurality of chest x-ray images and (ii) aplurality of bone suppressed chest x-ray images derived from theplurality of chest x-ray images.

This brief description of the invention is intended only to provide abrief overview of subject matter disclosed herein according to one ormore illustrative embodiments, and does not serve as a guide tointerpreting the claims or to define or limit the scope of theinvention, which is defined only by the appended claims. This briefdescription is provided to introduce an illustrative selection ofconcepts in a simplified form that are further described below in thedetailed description. This brief description is not intended to identifykey features or essential features of the claimed subject matter, nor isit intended to be used as an aid in determining the scope of the claimedsubject matter. The claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in thebackground.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the features of the invention can beunderstood, a detailed description of the invention may be had byreference to certain embodiments, some of which are illustrated in theaccompanying drawings. It is to be noted, however, that the drawingsillustrate only certain embodiments of this invention and are thereforenot to be considered limiting of its scope, for the scope of theinvention encompasses other equally effective embodiments. The drawingsare not necessarily to scale, emphasis generally being placed uponillustrating the features of certain embodiments of the invention. Inthe drawings, like numerals are used to indicate like parts throughoutthe various views. Thus, for further understanding of the invention,reference can be made to the following detailed description, read inconnection with the drawings in which:

FIG. 1 is a schematic diagram of an exemplary x-ray imaging system;

FIGS. 2-2D are flowcharts in accordance with the present disclosure;

FIG. 3 is a flowchart in accordance with the present disclosure;

FIG. 4 illustrates the generator illustrated in FIG. 3;

FIG. 5 illustrates the discriminator illustrated in FIG. 3;

FIG. 6A is an exemplary chest x-ray image;

FIG. 6B is an exemplary rib suppressed image; and

FIG. 6C is an exemplary enhanced bone suppressed image after theapplication of deep learning in accordance with the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

FIG. 1 is a schematic diagram of an exemplary digital radiographic (DR)imaging system 100 that may be used to practice embodiments of thepresent invention disclosed herein. The DR imaging system 100 includesan x-ray source 102 configured to generate radiographic energy. Thex-ray source 102 may include a single x-ray source or it may includemultiple x-ray sources. The imaging system 100 further includes agenerally planar DR detector 104, although a curved detector may also beused. A computer system 106 having a processor, electronic memory, anddigital monitor configured to display images captured by the DR detector104 is electrically connected to the x-ray source 102 and DR detector104. The DR detector 104 may include a two dimensional array ofphotosensor cells arranged in electronically addressable rows andcolumns. The DR detector 104 may be positioned to receive x-rays 108passing through an object 110, such as a human patient, duringradiographic energy exposures, or radiographic firing of energy pulses,as emitted by the x-ray source 102 during an imaging procedure. As shownin FIG. 1, the radiographic imaging system 100 may use an x-ray source102 that emits collimated x-rays 108, e.g. a directed x-ray beam 109such as a cone beam having a field of view, selectively aimed at andpassing through a preselected region 111 of the object 110. The x-raybeam 109 may be attenuated by varying degrees along its plurality ofrays according to the internal 3D structure of the object 110, wherebythe attenuated rays are detected by the array of photosensitive cells inDR detector 104. The DR detector 104 is positioned, as much as possible,in a perpendicular relation to a substantially central path 112 of theplurality of rays 108 emitted by the x-ray source 102. The array ofphotosensitive cells (pixels) of DR detector 104 may be electronicallyread out (scanned) by their position according to column and row. Asused herein, the terms “column” and “row” refer to the vertical andhorizontal arrangement of the photosensor cells and, for clarity ofdescription, it will be assumed that the rows extend horizontally andthe columns extend vertically. However, the orientation of the columnsand rows is arbitrary and does not limit the scope of any embodimentsdisclosed herein. Furthermore, the term “object” may be illustrated as ahuman patient in the description of FIG. 1, however, an object of a DRimaging system 100 or 100, as the term is used herein, may be a human oran animal.

In one exemplary embodiment, the photosensitive cells of DR detector 104may be read out to capture one or a plurality of projection images undercontrol of a detector control circuit 107 so that the exposure data(digital images) from the array of detector 104 may be transmitted tothe computer system 106. Each photosensitive cell in the DR detector 104may independently detect and store an attenuation value which isgenerated by a charge level generated in proportion to an intensity, orenergy level, of the attenuated radiographic radiation, or x-rays,received and absorbed in the photosensitive cells. Thus, eachphotosensitive cell, when read-out, provides information, or anattenuation value, defining a pixel of a radiographic image that may bedigitally decoded by image processing electronics in the computer system106 and displayed by the monitor for viewing by a user. Image processingelectronics may be included within the DR detector 104 housing, wherebythe radiographic images may be relayed to a computer system 106 by cableor wirelessly via electromagnetic wave transmission. As shown in FIG. 1,the source 102 and DR detector 104 may be affixed to an exemplarystationary C-arm 101, or a rotating mechanism controlling such C-arm 101and configured to revolve the source 102 and detector 104 in either ofthe angular directions indicated by the arrow 103 about an imaging axisz that coincides with the object 110 while the DR detector captures aplurality of radiographic projection images of the object 110 at anumber of angular imaging positions as the C-arm rotates about theobject 110.

The computer system 106 includes a processor and electronic memory andmay communicate with a detector control circuit 107 and x-ray generator104 over a connected cable (wired) or, as described herein, the DRdetector 104 may be equipped with a wireless transmitter to transmitradiographic image data wirelessly to the computer system 106 for imageprocessing therein. The detector control 107 may also include aprocessor and electronic memory (not shown) to control operations of theDR detector 104 as described herein, or such control operations may beimplemented using the computer system 106 by use of programmedinstructions. Programmed instructions stored in memory accessible tocomputer system 106 may be executed to perform the reconstructionalgorithms described herein. The computer system may also be used tocontrol activation of the x-ray generator 105 and the x-ray source 102during a radiographic exposure, or scan, controlling an x-ray tubeelectric current magnitude, and thus the fluence of x-rays in x-ray beam109, and/or the x-ray tube voltage, and thus the energy level of thex-rays in x-ray beam 109.

The DR detector 104 may transmit image (pixel) data to the monitorcomputer system 106 based on the radiographic exposure data receivedfrom its array of photosensitive cells. Alternatively, the DR detectormay be equipped to process the image data and store it, or it may storeraw unprocessed image data, in local or remotely accessible memory. Thephotosensitive cells in DR detector 104 may each include a sensingelement sensitive to x-rays, i.e. it directly absorbs x-rays andgenerates an amount of charge carriers in proportion to a magnitude ofthe absorbed x-ray energy. A switching element may be configured to beselectively activated to read out the charge level of a correspondingx-ray sensing element. Alternatively, photosensitive cells of theindirect type may each include a sensing element sensitive to light raysin the visible spectrum, i.e. it absorbs light rays and generates anamount of charge carriers in proportion to a magnitude of the absorbedlight energy, and a switching element that is selectively activated toread the charge level of the corresponding sensing element. Ascintillator, or wavelength converter, is disposed over the lightsensitive sensing elements to convert incident x-ray radiographic energyto visible light energy. Thus, it should be noted that the DR detector104, in the embodiments disclosed herein, may include an indirect ordirect type of DR detector.

In one embodiment, the photosensitive cell array may be read out bysequentially switching rows of the photosensitive array to a conducting(on) state by means of read-out circuits. This digital image informationmay be subsequently processed by computer system 106 to yield a digitalprojection image which may then be digitally stored and immediatelydisplayed on a monitor, or it may be displayed at a later time byaccessing the digital electronic memory containing the stored image. Aprojection images captured and transmitted by the detector 104 may beaccessed by computer system 106 to generate a bone suppressed imageusing algorithms as described herein. The flat panel DR detector 104having an imaging array as described herein is capable of bothsingle-shot (e.g., static, projection) and continuous image acquisition.

One embodiment of the computer system 106 further includes varioussoftware modules and hardware components to be implemented forgenerating a bone suppressed radiographic image using the methodsdescribed herein. According to one aspect of the current invention, bonesuppressed images are generated using cone beam radiographic image data.

Referring to FIG. 2, there is shown a flowchart in accordance with oneembodiment of the present disclosure. As illustrated, the methodincludes step 152 wherein a medical radiographic image such as a digitalchest X-ray image (FIG. 6A) is accessed, received, as by digitaltransmission, or captured using an x-ray system 100 as described herein.At step 154, a rib or bone suppression algorithm is applied to theradiographic image so as to suppress ribs or bone structures in theimage, as illustrated herein below. The resulting radiographic image isa rib or bone suppressed radiographic image (FIG. 6B) generated at step156. At step 158, the original captured chest x-ray image, e.g. startingimage, and its counterpart bone-suppressed image, e.g., target image,are input to a deep learning convolution neural network module, whichare used by the network module for training purposes at step 160. Thetraining steps 152-160 may be repeated a finite number of times.

At step 162, a medical radiographic image such as a digital chest X-rayimage is provided to the trained deep learning convolution neuralnetwork which, at step 164, applies its learned algorithm, via thetraining procedure, to suppress bony structures in the provided medicalradiographic image to generate, at step 166, an enhanced bone-suppressedradiographic image derived from the provided medical radiographic image.

Preferably, the training of the deep learning module involves many pairsof chest x-ray images (starting images) and their counterpart bonesuppressed x-ray images (target images) using known bone suppressionalgorithms such as those in the prior art incorporated herein byreference. The module, or model, is trained to generate an outputradiographic image that is rib free, similar to the target bonesuppressed image generated from each input chest x-ray image. Each ofthe bone suppressed radiographic images are preferably derived directlyfrom its associated original radiographic projection chest image usingthe known bone suppression algorithms. In a preferred embodiment, thebone suppressed images are not derived using previous approaches wherebya radiographic subtraction procedure is applied or using othermanipulations of two images. For example, in other previous systems, apair of x-ray images may be obtained using two exposures of a patient attwo different x-ray source energies in a dual energy x-ray imagingsystem, wherein one of the two energies is less sensitive to bonystructures. Thus, the present invention does not require multipleexposures at any stage of its generation of bone suppressed images,including the training stage as illustrated in FIG. 2. That is, nointermediate radiographic image is generated—the bone suppressed imageis generated directly from an original projection chest x-ray image. Assuch, Applicants have recognized that for the methods described herein,the bone suppressed images are preferably derived directly fromradiographic chest images via the bone suppression algorithm,radiographic image manipulation or radiographic image filtering.Examples of such known algorithms, image manipulation or image filteringare described in the three prior art references to Huo incorporatedherein. Applicants refer herein to well known bone suppressionalgorithms and do not further describe details of such algorithms.

In one embodiment, another known technology using convolutional neuralnetworks (CNN) or Generative Adversarial Networks (GANs) as deeplearning modules, or models, may be trained to suppress bony structuresin radiographic images. The deep learning CNN module is comprised of alibrary of training images, particularly a plurality of image pairs, forexample: a chest x-ray image (starting image) and its correspondingcounterpart bone suppressed image (target). In a preferred arrangement,the deep learning module is trained using a plurality of radiographicimages and a plurality of bone-suppressed radiographic images derivedfrom at least some of the plurality of starting radiographic images.

In machine learning, the CNN is a class of deep, feed-forward artificialneural networks, often applied to analyzing visual imagery. CNNs use avariation of multilayer perceptrons, or processing elements, designed torequire minimal preprocessing. Convolutional networks were inspired bybiological processes in that the connectivity pattern between neuronsresembles the organization of an animal visual cortex. In oneembodiment, the present disclosure describes herein below using GANs tosuppress ribs in radiographic images in FIG. 3.

Referring now to the flow charts of FIGS. 2A-2D, there are shownalternative methods for the method of FIG. 2. FIG. 2A illustrates anexemplary x-ray imaging system 200A, which may resemble the x-rayimaging system of FIG. 1 in certain respects, wherein a library oftraining images comprising pairs of original captured radiographicimages and corresponding bone suppressed radiographic images, deriveddirectly from the original captured radiographic images, are stored indigital electronic memory 202. The x-ray imaging system 200A alsoincludes a neural network module stored in the digital electronic memory202 which is available for applying a learned bone suppression algorithmto radiographic images. The learned bone suppression algorithm may beapplied to radiographic images captured by the x-ray imaging system 200Aor to radiographic images captured elsewhere and provided to the x-rayimaging system 200A such as by providing access to electronic memorycontaining the radiographic images to be bone suppressed or bytransmitting the radiographic images to the x-ray imaging system 200A.The neural network module may be trained using the library of trainingimages. The x-ray imaging system 200A includes a processing system 204having a processor, digital memory, and a display for displayingradiographic images. The processor may be programmable to apply aconventional bone suppression algorithm, as disclosed in the patentsincorporated herein by reference, to radiographic images. The processormay be programmed to access a non-bone suppressed radiographic image 206and to apply the conventional bone suppression algorithm 208 thereto.The processor may be programmed to apply the bone suppression algorithmlearned by the neural network to the conventionally bone suppressedradiographic image 208 to generate an enhanced bone suppressedradiographic image 210 which is then capable of being output to thedisplay 212.

FIG. 2B illustrates an exemplary x-ray imaging system 200B, which mayresemble the x-ray imaging system of FIG. 1 in certain respects, whereina library of training images including pairs of original capturedradiographic images and corresponding bone suppressed radiographicimages, derived directly from the original captured radiographic images,are stored in digital electronic memory 220. The x-ray imaging system200B also includes a neural network module stored in the digitalelectronic memory 220 which is available for training and for applying alearned bone suppression algorithm to radiographic images. The learnedbone suppression algorithm may be applied to radiographic imagescaptured by the x-ray imaging system 200B or to radiographic imagescaptured elsewhere and provided to the x-ray imaging system 200B such asby providing access to electronic memory containing the radiographicimages to be bone suppressed or by transmitting the radiographic imagesto the x-ray imaging system 200B. The neural network module may betrained using the library of training images. The x-ray imaging system200B includes a processing system 222 having a processor, digitalmemory, and a display for displaying radiographic images. The processormay be programmable to store in the digital electronic memoryradiographic images 224 captured by the x-ray imaging system 200B and toapply a conventional bone suppression algorithm 226, as disclosed in thepatents incorporated herein by reference, to the stored radiographicimages 224 to generate bone suppressed radiographic images 228. Theprocessor may be programmed to store pairs of the captured radiographicimages 224 and corresponding bone suppressed radiographic images 228 inthe library of training images to be used by the neural network modulefor training.

FIG. 2C illustrates an exemplary x-ray imaging system 200C, which mayresemble the x-ray imaging system of FIG. 1 in certain respects, whereina library of training images comprising pairs of original capturedradiographic images and corresponding bone suppressed radiographicimages, derived directly from the original captured radiographic images,are stored in digital electronic memory 230. The x-ray imaging system200C also includes a neural network module stored in the digitalelectronic memory 230 which is available for being trained using thelibrary of training images and for applying a learned bone suppressionalgorithm to radiographic images. The learned bone suppression algorithmmay be applied to radiographic images captured by the x-ray imagingsystem 200C or to radiographic images captured elsewhere and provided tothe x-ray imaging system 200C such as by providing access to electronicmemory containing the radiographic images to be bone suppressed or bytransmitting the radiographic images to the x-ray imaging system 200C.The neural network module may be trained using the library of trainingimages. The x-ray imaging system 200C includes a processing system 232having a processor, digital memory, and a display for displayingradiographic images. The processor may be programmable to provide aradiographic image 234 to the neural network after it's trained 236 sothat the neural network generates a bone suppressed image by applyingits trained bone suppression programming to the radiographic image togenerate a neural network bone suppressed radiographic image 238.

FIG. 2D illustrates an exemplary x-ray imaging system 200D, which mayresemble the x-ray imaging system of FIG. 1 in certain respects, and maybe operated according to the methods described with reference to bothFIGS. 2B and 2C. The imaging system 200D includes a library of trainingimages comprising pairs of original captured radiographic images andcorresponding bone suppressed radiographic images, derived directly fromthe original captured radiographic images, stored in digital electronicmemory 240. The x-ray imaging system 200D also includes a neural networkmodule stored in the digital electronic memory 240 which is availablefor training and for applying a learned bone suppression algorithm toradiographic images. The learned bone suppression algorithm may beapplied to radiographic images captured by the x-ray imaging system 200Dor to radiographic images captured elsewhere and provided to the x-rayimaging system 200D such as by providing access to electronic memorycontaining the radiographic images to be bone suppressed or bytransmitting the radiographic images to the x-ray imaging system 200D.The neural network module may be trained using the library of trainingimages. The x-ray imaging system 200D includes a processing system 242having a processor, digital memory, and a display for displayingradiographic images. The processor may be programmable to store in thedigital electronic memory 240 radiographic images 243 captured by thex-ray imaging system 200D and to apply a conventional bone suppressionalgorithm 245, as disclosed in the patents incorporated herein byreference, to the stored radiographic images 243 to generate bonesuppressed radiographic images 247. The processor may be programmed tostore pairs of the captured radiographic images 243 and correspondingbone suppressed radiographic images 247 in the library of trainingimages to be used by the neural network module for training. Thus, thelibrary of training images is supplied by use of the x-ray imagingsystem 200D. The processor may further be programmable to provide theradiographic image 243 to the neural network after it's trained 246 sothat the neural network generates a bone suppressed image by applyingits trained bone suppression programming to the radiographic imageprovided by the processor to generate a neural network bone suppressedradiographic image 248 which may then further be conditioned asnecessary for display 250.

FIGS. 3-5 illustrate a known GANs type neural network module 306 thatmay be trained to generate enhanced bone suppressed radiographic imagesusing non-bone suppressed radiographic images as inputs. The neuralnetwork module 306 includes a generator 306 a and discriminator 306 billustrated in FIGS. 4 and 5, respectively. In a training scheme, theGANs type neural network module 306 may be said to iteratively executean optimization process by receiving a chest x-ray image 302 at thegenerator 306 a and repeatedly attempting to generate a rib-suppressedx-ray image as an output to a discriminator 306 b. The discriminator 306b receives the generator 306 a generated image and a target bonesuppressed image 304 which was generated from the chest x-ray image 302using a known bone suppression algorithm. The discriminator compares thegenerator 306 a generated x-ray image with the target x-ray image andscores the generator 306 a generated image. The generator 306 a uses thescoring data, in a known back propagation method, to repeatedly adjust aweighting scheme for image generation in the generator 306 a until thegenerator's bone suppressed image output to the discriminator 306 b isdetermined by the discriminator 306 b to be acceptable, such as image308, by satisfying a scoring threshold. FIG. 4 is a schematic diagram ofa known mapping function carried out by the generator 306 a, wherein thegenerator 306 a receives a chest x-ray image x_(s) and generates, via anetwork of encoding and decoding layers, a bone suppressed imageG(x_(s)). FIG. 5 is a schematic diagram of a known discriminator 306 bthat scores a comparison between the bone suppressed image from thegenerator 306 a G(x_(s)) and the target bone suppressed image x_(d). Thegenerator 306 a may continue to adjust the generated bone suppressedimages until a threshold acceptable score from the discriminator 306 bis achieved, whereby the neural network may be considered to havecompleted training. The generator/discriminator may cooperate using twodimensional segments of the generated bone suppressed image G(x_(s))rather than on an entire frame of data. In one embodiment, thediscriminator 306 b may be programmed to estimate whether bonesuppressed image data G(x_(s)) provide by generator 306 a was generatedby the generator 306 a or whether it originated from the target imagex_(d) as part of the discriminator 306 b scoring scheme. This type ofscoring may be said to comprise a contest between the generator 306 aand discriminator 306 b.

FIGS. 6A-6C are exemplary radiographic images as described herein above.FIG. 6A is an exemplary original projection image (starting image)captured by a radiographic imaging system. FIG. 6B is an exemplary bonesuppressed radiographic image version of FIG. 6A using the known bonesuppression algorithms disclosed in the prior art patents incorporatedherein by reference. FIG. 6C is an exemplary enhanced bone suppressedradiographic image generated by a trained neural network.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.), or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “service,” “circuit,” “circuitry,”“module,” and/or “system.” Furthermore, aspects of the present inventionmay take the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Program code and/or executable instructions embodied on a computerreadable medium may be transmitted using any appropriate medium,including but not limited to wireless, wireline, optical fiber cable,RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer (device), partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider).

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. Applicants have described a computer storageproduct having at least one computer storage medium having instructionsstored therein causing one or more computers to perform the methoddescribed above. Applicants have described a computer storage mediumhaving instructions stored therein for causing a computer to perform themethod described above. Applicants have described a computer productembodied in a computer readable medium for performing the steps of themethod described above.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

What is claimed is:
 1. A system comprising: a processor programmed to receive a collection of radiographic image pairs that include a plurality of previously captured radiographic images and a corresponding plurality of bone-suppressed radiographic images, wherein the bone-suppressed radiographic image in each pair was generated by applying a bone suppression algorithm on the previously captured radiographic image in said each pair; and a deep learning module, wherein the processor is further programmed to train the deep learning module using the collection of radiographic image pairs in a repetitive deep learning technique to produce a trained deep learning module, wherein the processor is configured to apply the trained deep learning module to the previously captured current radiographic images to generate corresponding enhanced bone-suppressed radiographic images.
 2. The system of claim 1, further comprising: an x-ray imaging system to capture the previously captured radiographic images in each radiographic image pair; a deep learning module configured to be trained on pairs of radiographic images; and the processor configured to apply a bone suppression algorithm to the previously captured radiographic images, to generate a bone suppressed version of each of the previously captured radiographic images, and to apply the trained deep learning module to either the captured radiographic image or the bone suppressed version of the captured radiographic image to generate an enhanced version of the bone suppressed version of the radiographic image.
 3. A system, comprising: an x-ray imaging system to capture a current radiographic image; a deep learning module trained on a plurality of previously captured radiographic images and a corresponding plurality of bone-suppressed radiographic images generated from the plurality of previously captured radiographic images; and a processor configured to apply the trained deep learning module to the captured current radiographic image to generate an enhanced bone-suppressed radiographic image.
 4. A computer implemented method, the method comprising: acquiring a digital radiographic image; applying a bone suppression algorithm to the acquired radiographic image to generate a bone suppressed radiographic image; providing a convolutional neural network module trained using a plurality of radiographic training images; applying the convolutional neural network module to the bone suppressed radiographic image to generate an enhanced bone suppressed radiographic image; and displaying, storing, or transmitting the enhanced bone suppressed radiographic image.
 5. The method of claim 4, further comprising: providing a plurality of digital radiographic images; providing a plurality of bone-suppressed digital radiographic images each generated from one of the plurality of digital radiographic images; and training the convolutional neural network module using the plurality of digital radiographic images and the plurality of bone-suppressed digital radiographic images generated therefrom.
 6. The method of claim 5, wherein the step of providing the plurality of digital radiographic images includes providing a plurality of digital chest x-ray images, and wherein the step of providing the plurality of bone-suppressed digital radiographic images includes providing a plurality of rib-suppressed digital radiographic images each generated from one of the plurality of digital chest x-ray images.
 7. The method of claim 5, wherein the step of providing the plurality of bone-suppressed digital radiographic images includes providing a plurality of rib suppressed chest x-ray images derived directly from a plurality of chest x-ray images. 